Overview

Dataset statistics

Number of variables13
Number of observations65
Missing cells3
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory112.0 B

Variable types

TimeSeries12
Numeric1

Timeseries statistics

Number of series12
Time series length65
Starting point1960
Ending point2024
Period1
2026-01-16T10:14:48.073168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:48.365463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

Exports_USD is highly overall correlated with GDP_Current_USD and 9 other fieldsHigh correlation
GDP_Current_USD is highly overall correlated with Exports_USD and 7 other fieldsHigh correlation
GDP_Growth_Percent is highly overall correlated with Inflation_Rate_PercentHigh correlation
GDP_Per_Capita_USD is highly overall correlated with Exports_USD and 7 other fieldsHigh correlation
Imports_USD is highly overall correlated with Exports_USD and 7 other fieldsHigh correlation
Industry_Value_Added_Percent_GDP is highly overall correlated with Trade_Percent_GDPHigh correlation
Inflation_Rate_Percent is highly overall correlated with Exports_USD and 7 other fieldsHigh correlation
Official_Exchange_Rate is highly overall correlated with Exports_USD and 7 other fieldsHigh correlation
Population_Growth_Percent is highly overall correlated with Exports_USD and 4 other fieldsHigh correlation
Population_Total is highly overall correlated with Exports_USD and 8 other fieldsHigh correlation
Trade_Percent_GDP is highly overall correlated with Exports_USD and 1 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with Exports_USD and 8 other fieldsHigh correlation
Year is highly overall correlated with Exports_USD and 8 other fieldsHigh correlation
GDP_Growth_Percent has 1 (1.5%) missing valuesMissing
Official_Exchange_Rate has 1 (1.5%) missing valuesMissing
Population_Growth_Percent has 1 (1.5%) missing valuesMissing
Unnamed: 0 is non stationaryNon stationary
Year is non stationaryNon stationary
GDP_Current_USD is non stationaryNon stationary
GDP_Per_Capita_USD is non stationaryNon stationary
Official_Exchange_Rate is non stationaryNon stationary
Population_Total is non stationaryNon stationary
Population_Growth_Percent is non stationaryNon stationary
Exports_USD is non stationaryNon stationary
Imports_USD is non stationaryNon stationary
Unnamed: 0 is uniformly distributedUniform
Year is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Year has unique valuesUnique
GDP_Current_USD has unique valuesUnique
GDP_Per_Capita_USD has unique valuesUnique
Inflation_Rate_Percent has unique valuesUnique
Population_Total has unique valuesUnique
Exports_USD has unique valuesUnique
Imports_USD has unique valuesUnique
Trade_Percent_GDP has unique valuesUnique
Industry_Value_Added_Percent_GDP has unique valuesUnique
Unnamed: 0 has 1 (1.5%) zerosZeros

Reproduction

Analysis started2026-01-16 05:14:12.508333
Analysis finished2026-01-16 05:14:47.949589
Duration35.44 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Unnamed: 0
Numeric time series

High correlation  Non stationary  Uniform  Unique  Zeros 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32
Minimum0
Maximum64
Zeros1
Zeros (%)1.5%
Memory size1.0 KiB
2026-01-16T10:14:48.915378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2
Q116
median32
Q348
95-th percentile60.8
Maximum64
Range64
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.90767
Coefficient of variation (CV)0.5908647
Kurtosis-1.2
Mean32
Median Absolute Deviation (MAD)16
Skewness0
Sum2080
Variance357.5
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.921255366
2026-01-16T10:14:49.187737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:14:50.187487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:14:50.341046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01
 
1.5%
11
 
1.5%
21
 
1.5%
31
 
1.5%
41
 
1.5%
51
 
1.5%
61
 
1.5%
71
 
1.5%
81
 
1.5%
91
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
01
1.5%
11
1.5%
21
1.5%
31
1.5%
41
1.5%
51
1.5%
61
1.5%
71
1.5%
81
1.5%
91
1.5%
ValueCountFrequency (%)
641
1.5%
631
1.5%
621
1.5%
611
1.5%
601
1.5%
591
1.5%
581
1.5%
571
1.5%
561
1.5%
551
1.5%
2026-01-16T10:14:49.439394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Year
Numeric time series

High correlation  Non stationary  Uniform  Unique 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1992
Minimum1960
Maximum2024
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:14:50.554959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1963.2
Q11976
median1992
Q32008
95-th percentile2020.8
Maximum2024
Range64
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.90767
Coefficient of variation (CV)0.0094918024
Kurtosis-1.2
Mean1992
Median Absolute Deviation (MAD)16
Skewness0
Sum129480
Variance357.5
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.9585320861
2026-01-16T10:14:50.802038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:14:51.260277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:14:51.358041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
19601
 
1.5%
19611
 
1.5%
19621
 
1.5%
19631
 
1.5%
19641
 
1.5%
19651
 
1.5%
19661
 
1.5%
19671
 
1.5%
19681
 
1.5%
19691
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
19601
1.5%
19611
1.5%
19621
1.5%
19631
1.5%
19641
1.5%
19651
1.5%
19661
1.5%
19671
1.5%
19681
1.5%
19691
1.5%
ValueCountFrequency (%)
20241
1.5%
20231
1.5%
20221
1.5%
20211
1.5%
20201
1.5%
20191
1.5%
20181
1.5%
20171
1.5%
20161
1.5%
20151
1.5%
2026-01-16T10:14:50.931169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

GDP_Current_USD
Numeric time series

High correlation  Non stationary  Unique 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8937205 × 1011
Minimum4.1991344 × 109
Maximum6.4401932 × 1011
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:14:51.536827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.1991344 × 109
5-th percentile5.0188715 × 109
Q16.3743623 × 1010
median1.2305786 × 1011
Q33.479884 × 1011
95-th percentile4.9773379 × 1011
Maximum6.4401932 × 1011
Range6.3982018 × 1011
Interquartile range (IQR)2.8424478 × 1011

Descriptive statistics

Standard deviation1.7808503 × 1011
Coefficient of variation (CV)0.94039765
Kurtosis-0.3199341
Mean1.8937205 × 1011
Median Absolute Deviation (MAD)1.0191251 × 1011
Skewness0.93571901
Sum1.2309184 × 1013
Variance3.1714279 × 1022
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9540794029
2026-01-16T10:14:51.737840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:14:52.252656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:14:52.561183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
41991343901
 
1.5%
44269490951
 
1.5%
46935664161
 
1.5%
49286280181
 
1.5%
53798456481
 
1.5%
61973199291
 
1.5%
67899386721
 
1.5%
75553836901
 
1.5%
86231729601
 
1.5%
97430896071
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
41991343901
1.5%
44269490951
1.5%
46935664161
1.5%
49286280181
1.5%
53798456481
1.5%
61973199291
1.5%
67899386721
1.5%
75553836901
1.5%
86231729601
1.5%
97430896071
1.5%
ValueCountFrequency (%)
6.44019315 × 10111
1.5%
6.290822575 × 10111
1.5%
5.102398934 × 10111
1.5%
5.003998398 × 10111
1.5%
4.870695705 × 10111
1.5%
4.786180649 × 10111
1.5%
4.752520892 × 10111
1.5%
4.622847933 × 10111
1.5%
4.575104823 × 10111
1.5%
4.226622615 × 10111
1.5%
2026-01-16T10:14:51.871961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

GDP_Growth_Percent
Real number (ℝ)

High correlation  Missing 

Distinct64
Distinct (%)100.0%
Missing1
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean4.0607831
Minimum-21.599649
Maximum23.171246
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)26.2%
Memory size1.0 KiB
2026-01-16T10:14:52.749764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-21.599649
5-th percentile-9.3881473
Q1-0.21063483
median4.3447384
Q38.5162458
95-th percentile15.364012
Maximum23.171246
Range44.770895
Interquartile range (IQR)8.7268806

Descriptive statistics

Standard deviation7.8504622
Coefficient of variation (CV)1.9332385
Kurtosis1.2202237
Mean4.0607831
Median Absolute Deviation (MAD)4.3824541
Skewness-0.4733333
Sum259.89012
Variance61.629757
MonotonicityNot monotonic
2026-01-16T10:14:52.980341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.390918351
 
1.5%
7.909267931
 
1.5%
7.0420752291
 
1.5%
8.4752282491
 
1.5%
17.035711521
 
1.5%
11.502407281
 
1.5%
11.25280311
 
1.5%
14.404182681
 
1.5%
15.516658911
 
1.5%
10.927601631
 
1.5%
Other values (54)54
83.1%
ValueCountFrequency (%)
-21.599649351
1.5%
-12.840856671
1.5%
-12.020760251
1.5%
-9.7849055781
1.5%
-7.1398504991
1.5%
-6.081996621
1.5%
-5.6967872331
1.5%
-3.7479601571
1.5%
-3.7471713991
1.5%
-2.7735776921
1.5%
ValueCountFrequency (%)
23.171245541
1.5%
18.260435631
1.5%
17.035711521
1.5%
15.516658911
1.5%
14.499015851
1.5%
14.404182681
1.5%
13.701582991
1.5%
13.594926441
1.5%
12.716148441
1.5%
11.502407281
1.5%

GDP_Per_Capita_USD
Numeric time series

High correlation  Non stationary  Unique 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2731.6821
Minimum195.57753
Maximum8114.081
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:14:53.167886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum195.57753
5-th percentile211.99385
Q11408.9657
median2208.9964
Q34064.3467
95-th percentile6178.3803
Maximum8114.081
Range7918.5035
Interquartile range (IQR)2655.381

Descriptive statistics

Standard deviation2026.9671
Coefficient of variation (CV)0.74202159
Kurtosis-0.13472469
Mean2731.6821
Median Absolute Deviation (MAD)1409.9196
Skewness0.72465557
Sum177559.34
Variance4108595.6
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.5218206484
2026-01-16T10:14:53.365496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:14:53.865640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:14:53.949102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
195.57752721
 
1.5%
200.08152931
 
1.5%
205.78680971
 
1.5%
209.55937631
 
1.5%
221.73172781
 
1.5%
247.52667551
 
1.5%
262.87768381
 
1.5%
283.73370581
 
1.5%
314.28545671
 
1.5%
344.60168751
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
195.57752721
1.5%
200.08152931
1.5%
205.78680971
1.5%
209.55937631
1.5%
221.73172781
1.5%
247.52667551
1.5%
262.87768381
1.5%
283.73370581
1.5%
314.28545671
1.5%
344.60168751
1.5%
ValueCountFrequency (%)
8114.0809981
1.5%
8025.6874341
1.5%
6291.1944371
1.5%
6222.7419481
1.5%
6000.9334641
1.5%
5710.5994711
1.5%
5672.0637291
1.5%
5415.5228061
1.5%
5377.4115071
1.5%
5190.169591
1.5%
2026-01-16T10:14:53.515658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Inflation_Rate_Percent
Numeric time series

High correlation  Unique 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.214504
Minimum-0.38814853
Maximum49.655986
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:14:54.098840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.38814853
5-th percentile0.89657847
Q19.8194867
median14.761509
Q325.410509
95-th percentile42.692682
Maximum49.655986
Range50.044134
Interquartile range (IQR)15.591022

Descriptive statistics

Standard deviation12.020398
Coefficient of variation (CV)0.69827151
Kurtosis0.10504249
Mean17.214504
Median Absolute Deviation (MAD)7.5160832
Skewness0.7296676
Sum1118.9428
Variance144.48997
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0411125485
2026-01-16T10:14:54.287570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:14:54.819731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:14:54.934136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9.8224108131
 
1.5%
3.1625643961
 
1.5%
0.72132057171
 
1.5%
0.37184960721
 
1.5%
3.8144895721
 
1.5%
2.1543748351
 
1.5%
-0.38814853221
 
1.5%
1.597610081
 
1.5%
0.6903605221
 
1.5%
3.5931945161
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
-0.38814853221
1.5%
0.37184960721
1.5%
0.6903605221
1.5%
0.72132057171
1.5%
1.597610081
1.5%
1.666870941
1.5%
2.1543748351
1.5%
3.1625643961
1.5%
3.5931945161
1.5%
3.8144895721
1.5%
ValueCountFrequency (%)
49.655985851
1.5%
44.579185761
1.5%
43.488464171
1.5%
43.389016271
1.5%
39.907345571
1.5%
36.603035521
1.5%
32.45587141
1.5%
31.447028421
1.5%
30.594139041
1.5%
28.937344021
1.5%
2026-01-16T10:14:54.424820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Official_Exchange_Rate
Numeric time series

High correlation  Missing  Non stationary 

Distinct49
Distinct (%)76.6%
Missing1
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean7962.5095
Minimum65.567841
Maximum42000
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:14:55.099144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum65.567841
5-th percentile67.72356
Q175.75
median90.562513
Q39317.9959
95-th percentile42000
Maximum42000
Range41934.432
Interquartile range (IQR)9242.2459

Descriptive statistics

Standard deviation13430.579
Coefficient of variation (CV)1.686727
Kurtosis1.6932993
Mean7962.5095
Median Absolute Deviation (MAD)24.017728
Skewness1.7516666
Sum509600.61
Variance1.8038046 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9988747487
2026-01-16T10:14:55.295108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
2026-01-16T10:14:55.766459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:14:55.849312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
75.7500000811
 
16.9%
420005
 
7.7%
70.492030982
 
3.1%
75.750000071
 
1.5%
67.641342251
 
1.5%
75.749970131
 
1.5%
67.654945531
 
1.5%
70.239069861
 
1.5%
70.633731881
 
1.5%
68.886041951
 
1.5%
Other values (39)39
60.0%
ValueCountFrequency (%)
65.567841291
1.5%
67.521730011
1.5%
67.641342251
1.5%
67.654945531
1.5%
68.112372711
1.5%
68.699764631
1.5%
68.886041951
1.5%
70.239069861
1.5%
70.492030982
3.1%
70.631848091
1.5%
ValueCountFrequency (%)
420005
7.7%
40864.329011
 
1.5%
33226.298151
 
1.5%
30914.852441
 
1.5%
29011.491381
 
1.5%
25941.664141
 
1.5%
18414.448011
 
1.5%
12175.547221
 
1.5%
10616.306641
 
1.5%
10254.176471
 
1.5%
2026-01-16T10:14:55.428143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Population_Total
Numeric time series

High correlation  Non stationary  Unique 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56488855
Minimum21470434
Maximum91567738
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:14:56.040206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21470434
5-th percentile23667776
Q134837271
median61184983
Q375514204
95-th percentile88309079
Maximum91567738
Range70097304
Interquartile range (IQR)40676933

Descriptive statistics

Standard deviation22096867
Coefficient of variation (CV)0.39117215
Kurtosis-1.3364574
Mean56488855
Median Absolute Deviation (MAD)19229703
Skewness-0.09695917
Sum3.6717756 × 109
Variance4.8827151 × 1014
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.7478498775
2026-01-16T10:14:56.232858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:14:57.162102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:14:57.251502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
214704341
 
1.5%
221257261
 
1.5%
228079071
 
1.5%
235190051
 
1.5%
242628591
 
1.5%
250369781
 
1.5%
258292701
 
1.5%
266284321
 
1.5%
274373911
 
1.5%
282734821
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
214704341
1.5%
221257261
1.5%
228079071
1.5%
235190051
1.5%
242628591
1.5%
250369781
1.5%
258292701
1.5%
266284321
1.5%
274373911
1.5%
282734821
1.5%
ValueCountFrequency (%)
915677381
1.5%
906087071
1.5%
895242461
1.5%
884554881
1.5%
877234431
1.5%
870516481
1.5%
861179981
1.5%
850267541
1.5%
838122281
1.5%
826193621
1.5%
2026-01-16T10:14:56.474603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Population_Growth_Percent
Numeric time series

High correlation  Missing  Non stationary 

Distinct64
Distinct (%)100.0%
Missing1
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2.2662534
Minimum0.19045085
Maximum5.2940498
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:14:57.399127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.19045085
5-th percentile0.82656822
Q11.2489433
median2.1837452
Q33.0528765
95-th percentile3.7509274
Maximum5.2940498
Range5.103599
Interquartile range (IQR)1.8039332

Descriptive statistics

Standard deviation1.147358
Coefficient of variation (CV)0.50627966
Kurtosis-0.42373036
Mean2.2662534
Median Absolute Deviation (MAD)0.92181669
Skewness0.37684821
Sum145.04022
Variance1.3164304
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6974030018
2026-01-16T10:14:57.598779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:14:58.052859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:14:58.139154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0064177981
 
1.5%
3.0366270171
 
1.5%
3.0701543631
 
1.5%
3.1137927171
 
1.5%
3.1407107171
 
1.5%
3.1154492951
 
1.5%
3.0471172131
 
1.5%
2.9927201031
 
1.5%
3.001761541
 
1.5%
3.0362469051
 
1.5%
Other values (54)54
83.1%
ValueCountFrequency (%)
0.19045084951
1.5%
0.3565753481
1.5%
0.76875749821
1.5%
0.82578098651
1.5%
0.83102923691
1.5%
1.0528692221
1.5%
1.0783172091
1.5%
1.0804324861
1.5%
1.1072451781
1.5%
1.1083888281
1.5%
ValueCountFrequency (%)
5.2940498191
1.5%
5.2485345361
1.5%
3.8927647771
1.5%
3.7616811091
1.5%
3.6899895231
1.5%
3.6733626591
1.5%
3.6222724111
1.5%
3.5843414721
1.5%
3.4375097171
1.5%
3.267081261
1.5%
2026-01-16T10:14:57.724229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Exports_USD
Numeric time series

High correlation  Non stationary  Unique 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1833874 × 1010
Minimum5.8154142 × 108
Maximum1.5685105 × 1011
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:14:58.274196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.8154142 × 108
5-th percentile7.8092704 × 108
Q11.1841502 × 1010
median2.1524983 × 1010
Q37.7652596 × 1010
95-th percentile1.2143816 × 1011
Maximum1.5685105 × 1011
Range1.5626951 × 1011
Interquartile range (IQR)6.5811094 × 1010

Descriptive statistics

Standard deviation4.4208836 × 1010
Coefficient of variation (CV)1.0567713
Kurtosis-0.34415156
Mean4.1833874 × 1010
Median Absolute Deviation (MAD)1.8352219 × 1010
Skewness1.0257685
Sum2.7192018 × 1012
Variance1.9544211 × 1021
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9314697595
2026-01-16T10:14:58.548837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:14:59.034248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:14:59.149215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
581541416.31
 
1.5%
607915176.71
 
1.5%
680443017.71
 
1.5%
755608234.81
 
1.5%
882202284.61
 
1.5%
10008842061
 
1.5%
11063792481
 
1.5%
13068198271
 
1.5%
15072604061
 
1.5%
17564924411
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
581541416.31
1.5%
607915176.71
1.5%
680443017.71
1.5%
755608234.81
1.5%
882202284.61
1.5%
10008842061
1.5%
11063792481
1.5%
13068198271
1.5%
15072604061
1.5%
17564924411
1.5%
ValueCountFrequency (%)
1.568510491 × 10111
1.5%
1.447452744 × 10111
1.5%
1.26015225 × 10111
1.5%
1.220868793 × 10111
1.5%
1.188432864 × 10111
1.5%
1.119288632 × 10111
1.5%
1.114003238 × 10111
1.5%
1.094440451 × 10111
1.5%
1.08060448 × 10111
1.5%
1.079219938 × 10111
1.5%
2026-01-16T10:14:58.702305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Imports_USD
Numeric time series

High correlation  Non stationary  Unique 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0959447 × 1010
Minimum6.141025 × 108
Maximum1.3919863 × 1011
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:14:59.399108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.141025 × 108
5-th percentile7.5984166 × 108
Q11.1695655 × 1010
median2.5746071 × 1010
Q37.35975 × 1010
95-th percentile1.1081222 × 1011
Maximum1.3919863 × 1011
Range1.3858453 × 1011
Interquartile range (IQR)6.1901845 × 1010

Descriptive statistics

Standard deviation3.9957085 × 1010
Coefficient of variation (CV)0.97552796
Kurtosis-0.48086314
Mean4.0959447 × 1010
Median Absolute Deviation (MAD)2.2818065 × 1010
Skewness0.91485113
Sum2.662364 × 1012
Variance1.5965687 × 1021
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9772019075
2026-01-16T10:14:59.649949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:15:00.134315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:15:00.215170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
730164938.51
 
1.5%
702251188.41
 
1.5%
642016253.81
 
1.5%
614102503.61
 
1.5%
878548557.81
 
1.5%
10210555981
 
1.5%
11958838231
 
1.5%
14853053381
 
1.5%
17688502741
 
1.5%
20509260651
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
614102503.61
1.5%
642016253.81
1.5%
702251188.41
1.5%
730164938.51
1.5%
878548557.81
1.5%
10210555981
1.5%
11958838231
1.5%
14853053381
1.5%
17688502741
1.5%
20509260651
1.5%
ValueCountFrequency (%)
1.391986341 × 10111
1.5%
1.354975006 × 10111
1.5%
1.165366242 × 10111
1.5%
1.111136161 × 10111
1.5%
1.096066361 × 10111
1.5%
1.087972388 × 10111
1.5%
1.041099922 × 10111
1.5%
1.02452976 × 10111
1.5%
9.869895986 × 10101
1.5%
9.43502 × 10101
1.5%
2026-01-16T10:14:59.795123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Trade_Percent_GDP
Numeric time series

High correlation  Unique 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.696413
Minimum14.144854
Maximum76.115623
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:15:00.375914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.144854
5-th percentile24.096096
Q135.144245
median41.609136
Q348.172687
95-th percentile59.233533
Maximum76.115623
Range61.97077
Interquartile range (IQR)13.028442

Descriptive statistics

Standard deviation11.268362
Coefficient of variation (CV)0.27024775
Kurtosis1.3048466
Mean41.696413
Median Absolute Deviation (MAD)6.5635502
Skewness0.36708425
Sum2710.2669
Variance126.97597
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.04150585321
2026-01-16T10:15:00.548964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:15:00.981166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:15:01.257610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
31.237541671
 
1.5%
29.595243521
 
1.5%
28.175999961
 
1.5%
27.790913281
 
1.5%
32.728649811
 
1.5%
32.626035571
 
1.5%
33.906978871
 
1.5%
36.955438391
 
1.5%
37.991939791
 
1.5%
39.078143171
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
14.14485391
1.5%
17.884853141
1.5%
21.583978841
1.5%
23.172391141
1.5%
27.790913281
1.5%
27.934137611
1.5%
27.935025711
1.5%
28.175999961
1.5%
29.228219651
1.5%
29.595243521
1.5%
ValueCountFrequency (%)
76.115623431
1.5%
71.404688111
1.5%
65.435081131
1.5%
59.888078361
1.5%
56.615349611
1.5%
54.440495261
1.5%
54.097886921
1.5%
53.167358711
1.5%
52.062130681
1.5%
51.313857331
1.5%
2026-01-16T10:15:00.680268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Industry_Value_Added_Percent_GDP
Numeric time series

High correlation  Unique 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.209642
Minimum24.807213
Maximum62.323334
Zeros0
Zeros (%)0.0%
Memory size1.0 KiB
2026-01-16T10:15:01.417026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24.807213
5-th percentile25.827078
Q133.541574
median38.776158
Q343.394904
95-th percentile53.574776
Maximum62.323334
Range37.516121
Interquartile range (IQR)9.8533295

Descriptive statistics

Standard deviation8.0750397
Coefficient of variation (CV)0.20594525
Kurtosis0.30893832
Mean39.209642
Median Absolute Deviation (MAD)5.2345842
Skewness0.45962599
Sum2548.6267
Variance65.206265
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.04377131576
2026-01-16T10:15:01.715541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-16T10:15:02.279820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2026-01-16T10:15:02.411076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
25.747575221
 
1.5%
27.828214721
 
1.5%
29.544960241
 
1.5%
30.862951781
 
1.5%
31.402055831
 
1.5%
32.827129241
 
1.5%
33.563230891
 
1.5%
35.185498361
 
1.5%
36.302945481
 
1.5%
38.538982561
 
1.5%
Other values (55)55
84.6%
ValueCountFrequency (%)
24.807213481
1.5%
24.909740331
1.5%
24.97876191
1.5%
25.747575221
1.5%
26.145089541
1.5%
27.828214721
1.5%
29.544960241
1.5%
30.555445731
1.5%
30.862951781
1.5%
31.402055831
1.5%
ValueCountFrequency (%)
62.32333421
1.5%
57.243022881
1.5%
56.732685491
1.5%
53.645641541
1.5%
53.291315271
1.5%
49.637249051
1.5%
48.259900871
1.5%
48.235663141
1.5%
47.668788781
1.5%
47.470472481
1.5%
2026-01-16T10:15:01.898920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

2026-01-16T10:14:45.283312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:13.274276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:15.612666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:17.612301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:19.754342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:21.905881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:24.055856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:26.109960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:28.186789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:30.622260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:35.270164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:39.882201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:42.694770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:45.431632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:13.399025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:15.762330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:17.760437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:19.947673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:22.059715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:24.184219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:26.255945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:28.318405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:30.819310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:35.582008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:40.115967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:42.815478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:45.592597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:13.687038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:15.914501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:17.886136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:20.328329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:22.214058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:24.337371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:26.395991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:28.489250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:31.001466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:35.922341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:40.371578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:42.964289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:45.734232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:14.050944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:16.060487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:18.044732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:20.473616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:22.374124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:24.484489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:26.539770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:28.642398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:31.206821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:36.225716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:40.648492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:43.198834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:45.849336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:14.233824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:16.196406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:18.188354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:20.585907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:22.524847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:24.624164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:26.667048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:28.834667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:31.370736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:36.513950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:40.862808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:43.420625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:45.998979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:14.410724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:16.360277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:18.335702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:20.741916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:22.695796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:24.770609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:26.821659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:29.038181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:31.569091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:36.798922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:41.071165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:43.662934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:46.123697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:14.559677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:16.504075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:18.486347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:20.891206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:22.834403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:24.923959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:26.956668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:29.260367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:31.757399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:37.110742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:41.305279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:44.031012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:46.249935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:14.687065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:16.654486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:18.775297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:21.007460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:23.002781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:25.063283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:27.295393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:29.416339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:31.981904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:37.359717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:41.511695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:44.203621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:46.388244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:14.843940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:16.807089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:18.919547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:21.165615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:23.174801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:25.310965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:27.453273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:29.599995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:32.294400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:38.174365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:41.715497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:44.407121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:46.523027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:14.973224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:16.940574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:19.068990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:21.330184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:23.340234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:25.476525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:27.583454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:29.828134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:32.536368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:38.474920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:41.915530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:44.597964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:46.872309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:15.137529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:17.136485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:19.239063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:21.468828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:23.518137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:25.643356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:27.733375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:30.057902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:32.889376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:38.786985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:42.115330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:44.810883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:46.999142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:15.286999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:17.299799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:19.416336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:21.618623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:23.692326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:25.806791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:27.896144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:30.250879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:33.167582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:39.043043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:42.345944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:45.056793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:47.138597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:15.449718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:17.436685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:19.576845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:21.752035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:23.856495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:25.956841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:28.016590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:30.399730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:34.238511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:39.466458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:42.495688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-16T10:14:45.149537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-16T10:15:02.606264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Exports_USDGDP_Current_USDGDP_Growth_PercentGDP_Per_Capita_USDImports_USDIndustry_Value_Added_Percent_GDPInflation_Rate_PercentOfficial_Exchange_RatePopulation_Growth_PercentPopulation_TotalTrade_Percent_GDPUnnamed: 0Year
Exports_USD1.0000.878-0.2980.8510.9040.4900.5210.731-0.6110.8890.6110.8890.889
GDP_Current_USD0.8781.000-0.3410.9610.9490.1390.5270.734-0.4740.9110.3090.9110.911
GDP_Growth_Percent-0.298-0.3411.000-0.419-0.3100.005-0.504-0.1850.088-0.339-0.052-0.339-0.339
GDP_Per_Capita_USD0.8510.961-0.4191.0000.9300.1660.5090.635-0.3040.8180.3250.8180.818
Imports_USD0.9040.949-0.3100.9301.0000.2190.5340.690-0.4850.8970.4850.8970.897
Industry_Value_Added_Percent_GDP0.4900.1390.0050.1660.2191.0000.1080.111-0.2730.1870.7840.1870.187
Inflation_Rate_Percent0.5210.527-0.5040.5090.5340.1081.0000.348-0.4760.6190.2210.6190.619
Official_Exchange_Rate0.7310.734-0.1850.6350.6900.1110.3481.000-0.6630.8260.2180.8260.826
Population_Growth_Percent-0.611-0.4740.088-0.304-0.485-0.273-0.476-0.6631.000-0.741-0.383-0.741-0.741
Population_Total0.8890.911-0.3390.8180.8970.1870.6190.826-0.7411.0000.4011.0001.000
Trade_Percent_GDP0.6110.309-0.0520.3250.4850.7840.2210.218-0.3830.4011.0000.4010.401
Unnamed: 00.8890.911-0.3390.8180.8970.1870.6190.826-0.7411.0000.4011.0001.000
Year0.8890.911-0.3390.8180.8970.1870.6190.826-0.7411.0000.4011.0001.000

Missing values

2026-01-16T10:14:47.361197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-16T10:14:47.586940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-16T10:14:47.831022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0YearGDP_Current_USDGDP_Growth_PercentGDP_Per_Capita_USDInflation_Rate_PercentOfficial_Exchange_RatePopulation_TotalPopulation_Growth_PercentExports_USDImports_USDTrade_Percent_GDPIndustry_Value_Added_Percent_GDP
1960019604.199134e+09NaN195.5775279.82241175.7521470434NaN5.815414e+087.301649e+0831.23754225.747575
1961119614.426949e+0910.390918200.0815293.16256475.75221257263.0064186.079152e+087.022512e+0829.59524427.828215
1962219624.693566e+097.909268205.7868100.72132175.75228079073.0366276.804430e+086.420163e+0828.17600029.544960
1963319634.928628e+097.042075209.5593760.37185075.75235190053.0701547.556082e+086.141025e+0827.79091330.862952
1964419645.379846e+098.475228221.7317283.81449075.75242628593.1137938.822023e+088.785486e+0832.72865031.402056
1965519656.197320e+0917.035712247.5266762.15437575.75250369783.1407111.000884e+091.021056e+0932.62603632.827129
1966619666.789939e+0911.502407262.877684-0.38814975.75258292703.1154491.106379e+091.195884e+0933.90697933.563231
1967719677.555384e+0911.252803283.7337061.59761075.75266284323.0471171.306820e+091.485305e+0936.95543835.185498
1968819688.623173e+0914.404183314.2854570.69036175.75274373912.9927201.507260e+091.768850e+0937.99194036.302945
1969919699.743090e+0915.516659344.6016873.59319575.75282734823.0017621.756492e+092.050926e+0939.07814338.538983
Unnamed: 0YearGDP_Current_USDGDP_Growth_PercentGDP_Per_Capita_USDInflation_Rate_PercentOfficial_Exchange_RatePopulation_TotalPopulation_Growth_PercentExports_USDImports_USDTrade_Percent_GDPIndustry_Value_Added_Percent_GDP
20155520154.091917e+11-1.4248854952.73355512.48468229011.491377826193621.3615957.660410e+108.470986e+1039.42258932.174740
20165620164.786181e+118.8150875710.5994717.24542530914.852436838122281.4334869.720628e+109.365340e+1039.87724234.322283
20175720175.102399e+113.0268556000.9334648.04492433226.298152850267541.4387041.114003e+111.087972e+1143.15569336.141393
20185820184.119033e+11-3.7479604783.01067318.01411840864.329010861179981.2752471.220869e+111.111136e+1156.61535037.148428
20195920193.479884e+11-2.3614353997.49354539.90734642000.000000870516481.0783177.765260e+109.144113e+1048.59177133.414925
20206020202.809343e+114.4418093202.50003530.59413942000.000000877234430.7687575.166488e+107.162166e+1043.88446737.209142
20216120214.073507e+114.1308974605.14881343.38901642000.000000884554880.8310298.969539e+108.413084e+1042.67238039.225877
20226220224.226623e+114.3528114721.20437143.48846442000.000000895242461.2010031.080604e+119.869896e+1048.91835141.218778
20236320234.575105e+115.3323515049.29931644.57918642000.000000906087071.2040821.094440e+111.165366e+1149.39355038.776158
20246420244.752521e+113.6631215190.16959032.455871NaN915677381.0528691.119289e+111.354975e+1152.06213136.069723